Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min


Note:- If you are working Locally using anaconda, please uncomment the following code and execute it.

In [ ]:
#!pip install yfinance==0.2.38
#!pip install pandas==2.2.2
#!pip install nbformat
In [ ]:
!pip install yfinance
!pip install bs4
!pip install nbformat
In [1]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

In [2]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [3]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.

Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [4]:
tesla = yf.Ticker('TSLA')

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [5]:
tesla_data = tesla.history(period = 'max')

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [6]:
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[6]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

In [25]:
url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm'

html_data = requests.get(url).text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser. Make sure to use the html_data with the content parameter as follow html_data.content .

In [26]:
soup = BeautifulSoup(html_data, 'html.parser')
In [27]:
soup
Out[27]:
<!DOCTYPE html>

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<meta charset="utf-8"/>
<meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/>
<link href="https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue" rel="canonical"/>
<title>Tesla Revenue 2010-2022 | TSLA | MacroTrends</title>
<meta content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2021 was &lt;strong&gt;$53.823B&lt;/strong&gt;, a &lt;strong&gt;70.67% increase&lt;/strong&gt; from 2020.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
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<meta content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2021 was &lt;strong&gt;$53.823B&lt;/strong&gt;, a &lt;strong&gt;70.67% increase&lt;/strong&gt; from 2020.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
				&lt;/ul&gt;" name="twitter:description"/>
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				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
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				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
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<h2 style="margin-left:0px; font-weight:600; color:#444;">Tesla Revenue 2010-2022 | TSLA</h2>
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<span style="color:#444; line-height: 1.8;">Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				<ul style="margin-top:10px;">
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<thead>
<tr>
<th colspan="2" style="text-align:center">Tesla Annual Revenue<br/><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2021</td>
<td style="text-align:center">$53,823</td>
</tr>
<tr>
<td style="text-align:center">2020</td>
<td style="text-align:center">$31,536</td>
</tr>
<tr>
<td style="text-align:center">2019</td>
<td style="text-align:center">$24,578</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">$21,461</td>
</tr>
<tr>
<td style="text-align:center">2017</td>
<td style="text-align:center">$11,759</td>
</tr>
<tr>
<td style="text-align:center">2016</td>
<td style="text-align:center">$7,000</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">$4,046</td>
</tr>
<tr>
<td style="text-align:center">2014</td>
<td style="text-align:center">$3,198</td>
</tr>
<tr>
<td style="text-align:center">2013</td>
<td style="text-align:center">$2,013</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">$413</td>
</tr>
<tr>
<td style="text-align:center">2011</td>
<td style="text-align:center">$204</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">$117</td>
</tr>
<tr>
<td style="text-align:center">2009</td>
<td style="text-align:center">$112</td>
</tr>
</tbody>
</table>
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<table class="historical_data_table table">
<thead>
<tr>
<th colspan="2" style="text-align:center">Tesla Quarterly Revenue<br/><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2022-09-30</td>
<td style="text-align:center">$21,454</td>
</tr>
<tr>
<td style="text-align:center">2022-06-30</td>
<td style="text-align:center">$16,934</td>
</tr>
<tr>
<td style="text-align:center">2022-03-31</td>
<td style="text-align:center">$18,756</td>
</tr>
<tr>
<td style="text-align:center">2021-12-31</td>
<td style="text-align:center">$17,719</td>
</tr>
<tr>
<td style="text-align:center">2021-09-30</td>
<td style="text-align:center">$13,757</td>
</tr>
<tr>
<td style="text-align:center">2021-06-30</td>
<td style="text-align:center">$11,958</td>
</tr>
<tr>
<td style="text-align:center">2021-03-31</td>
<td style="text-align:center">$10,389</td>
</tr>
<tr>
<td style="text-align:center">2020-12-31</td>
<td style="text-align:center">$10,744</td>
</tr>
<tr>
<td style="text-align:center">2020-09-30</td>
<td style="text-align:center">$8,771</td>
</tr>
<tr>
<td style="text-align:center">2020-06-30</td>
<td style="text-align:center">$6,036</td>
</tr>
<tr>
<td style="text-align:center">2020-03-31</td>
<td style="text-align:center">$5,985</td>
</tr>
<tr>
<td style="text-align:center">2019-12-31</td>
<td style="text-align:center">$7,384</td>
</tr>
<tr>
<td style="text-align:center">2019-09-30</td>
<td style="text-align:center">$6,303</td>
</tr>
<tr>
<td style="text-align:center">2019-06-30</td>
<td style="text-align:center">$6,350</td>
</tr>
<tr>
<td style="text-align:center">2019-03-31</td>
<td style="text-align:center">$4,541</td>
</tr>
<tr>
<td style="text-align:center">2018-12-31</td>
<td style="text-align:center">$7,226</td>
</tr>
<tr>
<td style="text-align:center">2018-09-30</td>
<td style="text-align:center">$6,824</td>
</tr>
<tr>
<td style="text-align:center">2018-06-30</td>
<td style="text-align:center">$4,002</td>
</tr>
<tr>
<td style="text-align:center">2018-03-31</td>
<td style="text-align:center">$3,409</td>
</tr>
<tr>
<td style="text-align:center">2017-12-31</td>
<td style="text-align:center">$3,288</td>
</tr>
<tr>
<td style="text-align:center">2017-09-30</td>
<td style="text-align:center">$2,985</td>
</tr>
<tr>
<td style="text-align:center">2017-06-30</td>
<td style="text-align:center">$2,790</td>
</tr>
<tr>
<td style="text-align:center">2017-03-31</td>
<td style="text-align:center">$2,696</td>
</tr>
<tr>
<td style="text-align:center">2016-12-31</td>
<td style="text-align:center">$2,285</td>
</tr>
<tr>
<td style="text-align:center">2016-09-30</td>
<td style="text-align:center">$2,298</td>
</tr>
<tr>
<td style="text-align:center">2016-06-30</td>
<td style="text-align:center">$1,270</td>
</tr>
<tr>
<td style="text-align:center">2016-03-31</td>
<td style="text-align:center">$1,147</td>
</tr>
<tr>
<td style="text-align:center">2015-12-31</td>
<td style="text-align:center">$1,214</td>
</tr>
<tr>
<td style="text-align:center">2015-09-30</td>
<td style="text-align:center">$937</td>
</tr>
<tr>
<td style="text-align:center">2015-06-30</td>
<td style="text-align:center">$955</td>
</tr>
<tr>
<td style="text-align:center">2015-03-31</td>
<td style="text-align:center">$940</td>
</tr>
<tr>
<td style="text-align:center">2014-12-31</td>
<td style="text-align:center">$957</td>
</tr>
<tr>
<td style="text-align:center">2014-09-30</td>
<td style="text-align:center">$852</td>
</tr>
<tr>
<td style="text-align:center">2014-06-30</td>
<td style="text-align:center">$769</td>
</tr>
<tr>
<td style="text-align:center">2014-03-31</td>
<td style="text-align:center">$621</td>
</tr>
<tr>
<td style="text-align:center">2013-12-31</td>
<td style="text-align:center">$615</td>
</tr>
<tr>
<td style="text-align:center">2013-09-30</td>
<td style="text-align:center">$431</td>
</tr>
<tr>
<td style="text-align:center">2013-06-30</td>
<td style="text-align:center">$405</td>
</tr>
<tr>
<td style="text-align:center">2013-03-31</td>
<td style="text-align:center">$562</td>
</tr>
<tr>
<td style="text-align:center">2012-12-31</td>
<td style="text-align:center">$306</td>
</tr>
<tr>
<td style="text-align:center">2012-09-30</td>
<td style="text-align:center">$50</td>
</tr>
<tr>
<td style="text-align:center">2012-06-30</td>
<td style="text-align:center">$27</td>
</tr>
<tr>
<td style="text-align:center">2012-03-31</td>
<td style="text-align:center">$30</td>
</tr>
<tr>
<td style="text-align:center">2011-12-31</td>
<td style="text-align:center">$39</td>
</tr>
<tr>
<td style="text-align:center">2011-09-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-06-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-03-31</td>
<td style="text-align:center">$49</td>
</tr>
<tr>
<td style="text-align:center">2010-12-31</td>
<td style="text-align:center">$36</td>
</tr>
<tr>
<td style="text-align:center">2010-09-30</td>
<td style="text-align:center">$31</td>
</tr>
<tr>
<td style="text-align:center">2010-06-30</td>
<td style="text-align:center">$28</td>
</tr>
<tr>
<td style="text-align:center">2010-03-31</td>
<td style="text-align:center">$21</td>
</tr>
<tr>
<td style="text-align:center">2009-12-31</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">2009-09-30</td>
<td style="text-align:center">$46</td>
</tr>
<tr>
<td style="text-align:center">2009-06-30</td>
<td style="text-align:center">$27</td>
</tr>
</tbody>
</table>
</div>
</div>
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<table class="historical_data_table table">
<thead>
<tr>
<th style="text-align:center">Sector</th>
<th style="text-align:center">Industry</th>
<th style="text-align:center">Market Cap</th>
<th style="text-align:center">Revenue</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"><a href="https://www.macrotrends.net/stocks/sector/5/auto-tires-trucks">Auto/Tires/Trucks</a></td>
<td style="text-align:center"><a href="https://www.macrotrends.net/stocks/industry/7/">Auto Manufacturers - Domestic</a></td>
<td style="text-align:center">$549.575B</td>
<td style="text-align:center">$53.823B</td>
</tr>
<tr>
<td colspan="4" style="padding:15px;">
<span>Tesla is the market leader in battery-powered electric car sales in the United States, with roughly 70% market share. The company's flagship Model 3 is the best-selling EV model in the United States. Tesla, which has managed to garner the reputation of a gold standard over the years, is now a far bigger entity that what it started off since its IPO in 2010, with its market cap crossing $1 trillion for the first time in October 2021.? The EV king's market capitalization is more than the combined value of legacy automakers including Toyota, Volkswagen, Daimler, General Motors and Ford.Over the years, Tesla has shifted from developing niche products for affluent buyers to making more affordable EVs for the masses. The firm's three-pronged business model approach of direct sales, servicing, and charging its EVs sets it apart from other carmakers. Tesla, which is touted as the clean energy revolutionary automaker, is much more than just a car manufacturer.</span>
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<td style="text-align:center">United States</td>
<td style="text-align:center">$53.930B</td>
<td style="text-align:center">5.56</td>
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<td style="text-align:center">$52.668B</td>
<td style="text-align:center">8.09</td>
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<td style="text-align:center">$6.762B</td>
<td style="text-align:center">9.56</td>
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<td style="text-align:center">$6.267B</td>
<td style="text-align:center">11.86</td>
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<td style="text-align:center">$5.134B</td>
<td style="text-align:center">16.40</td>
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<td style="text-align:center">United States</td>
<td style="text-align:center">$2.261B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:center">$0.551B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/VLTA/volta/revenue">Volta (VLTA)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.071B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/BRDS/bird-global/revenue">Bird Global (BRDS)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.054B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/ZEV/lightning-emotors/revenue">Lightning EMotors (ZEV)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.043B</td>
<td style="text-align:center">0.00</td>
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</html>

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Step-by-step instructions ``` Here are the step-by-step instructions: 1. Find All Tables: Start by searching for all HTML tables on a webpage using `soup.find_all('table')`. 2. Identify the Relevant Table: then loops through each table. If a table contains the text “Tesla Quarterly Revenue,”, select that table. 3. Initialize a DataFrame: Create an empty Pandas DataFrame called `tesla_revenue` with columns “Date” and “Revenue.” 4. Loop Through Rows: For each row in the relevant table, extract the data from the first and second columns (date and revenue). 5. Clean Revenue Data: Remove dollar signs and commas from the revenue value. 6. Add Rows to DataFrame: Create a new row in the DataFrame with the extracted date and cleaned revenue values. 7. Repeat for All Rows: Continue this process for all rows in the table. ```
Click here if you need help locating the table ``` Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab soup.find_all("tbody")[1] If you want to use the read_html function the table is located at index 1 We are focusing on quarterly revenue in the lab. > Note: Instead of using the deprecated pd.append() method, consider using pd.concat([df, pd.DataFrame], ignore_index=True). ```
In [37]:
read_html_pandas_data = pd.read_html(url)
In [38]:
tesla_revenue = read_html_pandas_data[1]
In [40]:
tesla_revenue.columns = ['Date', 'Revenue']
tesla_revenue.head()
Out[40]:
Date Revenue
0 2022-09-30 $21,454
1 2022-06-30 $16,934
2 2022-03-31 $18,756
3 2021-12-31 $17,719
4 2021-09-30 $13,757
In [ ]:
#tesla_revenue = pd.DataFrame(columns = ['Date','Revenue'])

#for row in soup.find('tbody').find_all('tr'):
#    col = row.find_all('td')
#    date = col[0].text
#    revenue = col[1].text
#    tesla_revenue = pd.concat([tesla_revenue,pd.DataFrame({"Date":[date], "Revenue":[revenue]})], ignore_index=True)
    
#tesla_revenue.head()

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [41]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"", regex=True)

Execute the following lines to remove an null or empty strings in the Revenue column.

In [42]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [43]:
tesla_revenue.tail()
Out[43]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [44]:
gamestop = yf.Ticker("GME")

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [45]:
gme_data = gamestop.history(period = 'max')

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [46]:
gme_data.reset_index(inplace = True)
gme_data.head()
Out[46]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620129 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.

In [51]:
url1 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'

html_data_2 = requests.get(url1).text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [52]:
soup1 = BeautifulSoup(html_data_2, 'html.parser')

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

Note: Use the method similar to what you did in question 2.

Click here if you need help locating the table ``` Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab soup.find_all("tbody")[1] If you want to use the read_html function the table is located at index 1 ```
In [55]:
html_pandas_data = pd.read_html(url1)
gme_revenue = html_pandas_data[1]

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [56]:
gme_revenue.columns = ['Date', 'Revenue']
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"", regex=True)
gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
gme_revenue.tail()
Out[56]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.

Hint ``` You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`. ```
In [57]:
make_graph(tesla_data,tesla_revenue,'Tesla')

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

Hint ``` You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')` ```
In [58]:
make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

© IBM Corporation 2020. All rights reserved.

```toggle ## Change Log

```toggle | Date (YYYY-MM-DD) | Version | Changed By    | Change Description        |

```toggle | ----------------- | ------- | ------------- | ------------------------- |

```toggle | 2022-02-28        | 1.2     | Lakshmi Holla | Changed the URL of GameStop |

```toggle | 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |

```toggle | 2020-08-27        | 1.0     | Malika Singla | Added lab to GitLab       |